Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations627920
Missing cells3008
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory302.6 MiB
Average record size in memory505.3 B

Variable types

Numeric7
Categorical3
Text3
DateTime1

Alerts

Country_Region has constant value "US"Constant
Confirmed is highly overall correlated with DeathsHigh correlation
Deaths is highly overall correlated with ConfirmedHigh correlation
FIPS is highly overall correlated with UIDHigh correlation
Lat is highly overall correlated with iso2 and 1 other fieldsHigh correlation
UID is highly overall correlated with FIPS and 2 other fieldsHigh correlation
code3 is highly overall correlated with iso2 and 1 other fieldsHigh correlation
iso2 is highly overall correlated with Lat and 3 other fieldsHigh correlation
iso3 is highly overall correlated with Lat and 3 other fieldsHigh correlation
iso2 is highly imbalanced (93.1%)Imbalance
iso3 is highly imbalanced (93.1%)Imbalance
Confirmed is highly skewed (γ1 = 39.35689783)Skewed
Deaths is highly skewed (γ1 = 63.59088753)Skewed
Lat has 20304 (3.2%) zerosZeros
Long_ has 20304 (3.2%) zerosZeros
Confirmed has 253223 (40.3%) zerosZeros
Deaths has 428930 (68.3%) zerosZeros

Reproduction

Analysis started2025-11-30 19:08:19.748512
Analysis finished2025-11-30 19:08:40.093828
Duration20.35 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

UID
Real number (ℝ)

High correlation 

Distinct3340
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83429580
Minimum16
Maximum84099999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-11-30T20:08:40.394547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile84002170
Q184018108
median84029208
Q384046120
95-th percentile84055081
Maximum84099999
Range84099983
Interquartile range (IQR)28011

Descriptive statistics

Standard deviation4314702.3
Coefficient of variation (CV)0.051716697
Kurtosis176.2819
Mean83429580
Median Absolute Deviation (MAD)12834
Skewness-11.171049
Sum5.2387102 × 1013
Variance1.8616656 × 1013
MonotonicityNot monotonic
2025-11-30T20:08:40.594832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16188
 
< 0.1%
84041013188
 
< 0.1%
84040015188
 
< 0.1%
84040017188
 
< 0.1%
84040019188
 
< 0.1%
84040021188
 
< 0.1%
84040023188
 
< 0.1%
84040025188
 
< 0.1%
84040027188
 
< 0.1%
84040029188
 
< 0.1%
Other values (3330)626040
99.7%
ValueCountFrequency (%)
16188
< 0.1%
316188
< 0.1%
580188
< 0.1%
850188
< 0.1%
63072001188
< 0.1%
63072003188
< 0.1%
63072005188
< 0.1%
63072007188
< 0.1%
63072009188
< 0.1%
63072011188
< 0.1%
ValueCountFrequency (%)
84099999188
< 0.1%
84090056188
< 0.1%
84090055188
< 0.1%
84090054188
< 0.1%
84090053188
< 0.1%
84090051188
< 0.1%
84090050188
< 0.1%
84090049188
< 0.1%
84090048188
< 0.1%
84090047188
< 0.1%

iso2
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.3 MiB
US
612128 
PR
 
15040
AS
 
188
GU
 
188
MP
 
188

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1255840
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAS
2nd rowGU
3rd rowMP
4th rowPR
5th rowPR

Common Values

ValueCountFrequency (%)
US612128
97.5%
PR15040
 
2.4%
AS188
 
< 0.1%
GU188
 
< 0.1%
MP188
 
< 0.1%
VI188
 
< 0.1%

Length

2025-11-30T20:08:40.757675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T20:08:40.879823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
us612128
97.5%
pr15040
 
2.4%
as188
 
< 0.1%
gu188
 
< 0.1%
mp188
 
< 0.1%
vi188
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
U612316
48.8%
S612316
48.8%
P15228
 
1.2%
R15040
 
1.2%
A188
 
< 0.1%
G188
 
< 0.1%
M188
 
< 0.1%
V188
 
< 0.1%
I188
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1255840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U612316
48.8%
S612316
48.8%
P15228
 
1.2%
R15040
 
1.2%
A188
 
< 0.1%
G188
 
< 0.1%
M188
 
< 0.1%
V188
 
< 0.1%
I188
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1255840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U612316
48.8%
S612316
48.8%
P15228
 
1.2%
R15040
 
1.2%
A188
 
< 0.1%
G188
 
< 0.1%
M188
 
< 0.1%
V188
 
< 0.1%
I188
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1255840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U612316
48.8%
S612316
48.8%
P15228
 
1.2%
R15040
 
1.2%
A188
 
< 0.1%
G188
 
< 0.1%
M188
 
< 0.1%
V188
 
< 0.1%
I188
 
< 0.1%

iso3
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.9 MiB
USA
612128 
PRI
 
15040
ASM
 
188
GUM
 
188
MNP
 
188

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1883760
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASM
2nd rowGUM
3rd rowMNP
4th rowPRI
5th rowPRI

Common Values

ValueCountFrequency (%)
USA612128
97.5%
PRI15040
 
2.4%
ASM188
 
< 0.1%
GUM188
 
< 0.1%
MNP188
 
< 0.1%
VIR188
 
< 0.1%

Length

2025-11-30T20:08:41.026799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T20:08:41.176678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
usa612128
97.5%
pri15040
 
2.4%
asm188
 
< 0.1%
gum188
 
< 0.1%
mnp188
 
< 0.1%
vir188
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
U612316
32.5%
S612316
32.5%
A612316
32.5%
P15228
 
0.8%
R15228
 
0.8%
I15228
 
0.8%
M564
 
< 0.1%
G188
 
< 0.1%
N188
 
< 0.1%
V188
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1883760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U612316
32.5%
S612316
32.5%
A612316
32.5%
P15228
 
0.8%
R15228
 
0.8%
I15228
 
0.8%
M564
 
< 0.1%
G188
 
< 0.1%
N188
 
< 0.1%
V188
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1883760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U612316
32.5%
S612316
32.5%
A612316
32.5%
P15228
 
0.8%
R15228
 
0.8%
I15228
 
0.8%
M564
 
< 0.1%
G188
 
< 0.1%
N188
 
< 0.1%
V188
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1883760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U612316
32.5%
S612316
32.5%
A612316
32.5%
P15228
 
0.8%
R15228
 
0.8%
I15228
 
0.8%
M564
 
< 0.1%
G188
 
< 0.1%
N188
 
< 0.1%
V188
 
< 0.1%

code3
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean834.49162
Minimum16
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-11-30T20:08:41.313681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile840
Q1840
median840
Q3840
95-th percentile840
Maximum850
Range834
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36.49262
Coefficient of variation (CV)0.043730362
Kurtosis109.29686
Mean834.49162
Median Absolute Deviation (MAD)0
Skewness-8.5497067
Sum5.2399398 × 108
Variance1331.7113
MonotonicityNot monotonic
2025-11-30T20:08:41.453051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
840612128
97.5%
63015040
 
2.4%
16188
 
< 0.1%
316188
 
< 0.1%
580188
 
< 0.1%
850188
 
< 0.1%
ValueCountFrequency (%)
16188
 
< 0.1%
316188
 
< 0.1%
580188
 
< 0.1%
63015040
 
2.4%
840612128
97.5%
850188
 
< 0.1%
ValueCountFrequency (%)
850188
 
< 0.1%
840612128
97.5%
63015040
 
2.4%
580188
 
< 0.1%
316188
 
< 0.1%
16188
 
< 0.1%

FIPS
Real number (ℝ)

High correlation 

Distinct3330
Distinct (%)0.5%
Missing1880
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean33061.685
Minimum60
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-11-30T20:08:41.623248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile5103
Q119079
median31014
Q347131
95-th percentile72035
Maximum99999
Range99939
Interquartile range (IQR)28052

Descriptive statistics

Standard deviation18636.157
Coefficient of variation (CV)0.56367838
Kurtosis0.39039103
Mean33061.685
Median Absolute Deviation (MAD)13902
Skewness0.60632612
Sum2.0697937 × 1010
Variance3.4730634 × 108
MonotonicityNot monotonic
2025-11-30T20:08:41.797907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46109188
 
< 0.1%
40003188
 
< 0.1%
40005188
 
< 0.1%
40007188
 
< 0.1%
40009188
 
< 0.1%
40011188
 
< 0.1%
40013188
 
< 0.1%
40015188
 
< 0.1%
40017188
 
< 0.1%
40019188
 
< 0.1%
Other values (3320)624160
99.4%
(Missing)1880
 
0.3%
ValueCountFrequency (%)
60188
< 0.1%
66188
< 0.1%
69188
< 0.1%
78188
< 0.1%
1001188
< 0.1%
1003188
< 0.1%
1005188
< 0.1%
1007188
< 0.1%
1009188
< 0.1%
1011188
< 0.1%
ValueCountFrequency (%)
99999188
< 0.1%
90056188
< 0.1%
90055188
< 0.1%
90054188
< 0.1%
90053188
< 0.1%
90051188
< 0.1%
90050188
< 0.1%
90049188
< 0.1%
90048188
< 0.1%
90047188
< 0.1%

Admin2
Text

Distinct1978
Distinct (%)0.3%
Missing1128
Missing (%)0.2%
Memory size38.4 MiB
2025-11-30T20:08:42.078543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length41
Median length21
Mean length7.1541692
Min length3

Characters and Unicode

Total characters4484176
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdjuntas
2nd rowAguada
3rd rowAguadilla
4th rowAguas Buenas
5th rowAibonito
ValueCountFrequency (%)
of10716
 
1.5%
unassigned9776
 
1.4%
out9776
 
1.4%
washington5828
 
0.8%
jefferson5264
 
0.8%
st4888
 
0.7%
franklin4888
 
0.7%
jackson4512
 
0.7%
lincoln4512
 
0.7%
san3948
 
0.6%
Other values (1999)629800
90.8%
2025-11-30T20:08:42.541621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a443304
 
9.9%
e421120
 
9.4%
n376940
 
8.4%
o341408
 
7.6%
r292904
 
6.5%
l244212
 
5.4%
i235940
 
5.3%
s208492
 
4.6%
t203980
 
4.5%
u119944
 
2.7%
Other values (48)1595932
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)4484176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a443304
 
9.9%
e421120
 
9.4%
n376940
 
8.4%
o341408
 
7.6%
r292904
 
6.5%
l244212
 
5.4%
i235940
 
5.3%
s208492
 
4.6%
t203980
 
4.5%
u119944
 
2.7%
Other values (48)1595932
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4484176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a443304
 
9.9%
e421120
 
9.4%
n376940
 
8.4%
o341408
 
7.6%
r292904
 
6.5%
l244212
 
5.4%
i235940
 
5.3%
s208492
 
4.6%
t203980
 
4.5%
u119944
 
2.7%
Other values (48)1595932
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4484176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a443304
 
9.9%
e421120
 
9.4%
n376940
 
8.4%
o341408
 
7.6%
r292904
 
6.5%
l244212
 
5.4%
i235940
 
5.3%
s208492
 
4.6%
t203980
 
4.5%
u119944
 
2.7%
Other values (48)1595932
35.6%
Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 MiB
2025-11-30T20:08:42.774031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length16
Mean length8.1739521
Min length4

Characters and Unicode

Total characters5132588
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmerican Samoa
2nd rowGuam
3rd rowNorthern Mariana Islands
4th rowPuerto Rico
5th rowPuerto Rico
ValueCountFrequency (%)
texas48128
 
6.6%
virginia36096
 
4.9%
georgia30268
 
4.1%
north29516
 
4.0%
carolina28200
 
3.8%
new25192
 
3.4%
dakota23124
 
3.2%
kentucky22936
 
3.1%
missouri22184
 
3.0%
south21808
 
3.0%
Other values (57)446312
60.8%
2025-11-30T20:08:43.174795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a674168
13.1%
i548960
 
10.7%
o428828
 
8.4%
n423940
 
8.3%
s415668
 
8.1%
e316404
 
6.2%
r270156
 
5.3%
t179916
 
3.5%
l158672
 
3.1%
h129720
 
2.5%
Other values (36)1586156
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)5132588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a674168
13.1%
i548960
 
10.7%
o428828
 
8.4%
n423940
 
8.3%
s415668
 
8.1%
e316404
 
6.2%
r270156
 
5.3%
t179916
 
3.5%
l158672
 
3.1%
h129720
 
2.5%
Other values (36)1586156
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5132588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a674168
13.1%
i548960
 
10.7%
o428828
 
8.4%
n423940
 
8.3%
s415668
 
8.1%
e316404
 
6.2%
r270156
 
5.3%
t179916
 
3.5%
l158672
 
3.1%
h129720
 
2.5%
Other values (36)1586156
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5132588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a674168
13.1%
i548960
 
10.7%
o428828
 
8.4%
n423940
 
8.3%
s415668
 
8.1%
e316404
 
6.2%
r270156
 
5.3%
t179916
 
3.5%
l158672
 
3.1%
h129720
 
2.5%
Other values (36)1586156
30.9%

Country_Region
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.3 MiB
US
627920 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1255840
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US627920
100.0%

Length

2025-11-30T20:08:43.327808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T20:08:43.437430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
us627920
100.0%

Most occurring characters

ValueCountFrequency (%)
U627920
50.0%
S627920
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1255840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U627920
50.0%
S627920
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1255840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U627920
50.0%
S627920
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1255840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U627920
50.0%
S627920
50.0%

Lat
Real number (ℝ)

High correlation  Zeros 

Distinct3226
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.707212
Minimum-14.271
Maximum69.314792
Zeros20304
Zeros (%)3.2%
Negative188
Negative (%)< 0.1%
Memory size4.8 MiB
2025-11-30T20:08:43.577926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-14.271
5-th percentile18.344964
Q133.895587
median38.002344
Q341.573069
95-th percentile46.466812
Maximum69.314792
Range83.585792
Interquartile range (IQR)7.6774818

Descriptive statistics

Standard deviation9.0615719
Coefficient of variation (CV)0.2468608
Kurtosis7.137463
Mean36.707212
Median Absolute Deviation (MAD)3.8387588
Skewness-2.1144867
Sum23049193
Variance82.112085
MonotonicityNot monotonic
2025-11-30T20:08:43.741533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020304
 
3.2%
40.12491499376
 
0.1%
39.37231946376
 
0.1%
37.85447192376
 
0.1%
38.99617072376
 
0.1%
41.52106798376
 
0.1%
41.27116049376
 
0.1%
41.40674725376
 
0.1%
39.96995815188
 
< 0.1%
39.56021306188
 
< 0.1%
Other values (3216)604608
96.3%
ValueCountFrequency (%)
-14.271188
 
< 0.1%
020304
3.2%
13.4443188
 
< 0.1%
15.0979188
 
< 0.1%
17.982429188
 
< 0.1%
17.994525188
 
< 0.1%
17.998457188
 
< 0.1%
18.007516188
 
< 0.1%
18.010387188
 
< 0.1%
18.011661188
 
< 0.1%
ValueCountFrequency (%)
69.31479216188
< 0.1%
67.04919196188
< 0.1%
65.50815459188
< 0.1%
64.90320724188
< 0.1%
64.80726247188
< 0.1%
63.87692095188
< 0.1%
63.67264044188
< 0.1%
62.31305045188
< 0.1%
62.1542916188
< 0.1%
61.47502768188
< 0.1%

Long_
Real number (ℝ)

Zeros 

Distinct3226
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-88.601474
Minimum-174.1596
Maximum145.6739
Zeros20304
Zeros (%)3.2%
Negative607240
Negative (%)96.7%
Memory size4.8 MiB
2025-11-30T20:08:43.916053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-174.1596
5-th percentile-117.54927
Q1-97.790204
median-89.48671
Q3-82.311265
95-th percentile-66.789985
Maximum145.6739
Range319.8335
Interquartile range (IQR)15.478939

Descriptive statistics

Standard deviation21.715747
Coefficient of variation (CV)-0.24509465
Kurtosis15.177245
Mean-88.601474
Median Absolute Deviation (MAD)7.7586698
Skewness2.493298
Sum-55634638
Variance471.57368
MonotonicityNot monotonic
2025-11-30T20:08:44.116315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020304
 
3.2%
-109.5174415376
 
0.1%
-111.5758676376
 
0.1%
-111.4418764376
 
0.1%
-110.7013958376
 
0.1%
-113.0832816376
 
0.1%
-111.9145117376
 
0.1%
-70.68763497376
 
0.1%
-83.01115755188
 
< 0.1%
-83.4562016188
 
< 0.1%
Other values (3216)604608
96.3%
ValueCountFrequency (%)
-174.1596188
< 0.1%
-170.132188
< 0.1%
-164.0353804188
< 0.1%
-163.3967883188
< 0.1%
-161.9722021188
< 0.1%
-159.8561831188
< 0.1%
-159.7503946188
< 0.1%
-159.5966786188
< 0.1%
-158.2381942188
< 0.1%
-157.9712182188
< 0.1%
ValueCountFrequency (%)
145.6739188
 
< 0.1%
144.7937188
 
< 0.1%
020304
3.2%
-64.8963188
 
< 0.1%
-65.28813188
 
< 0.1%
-65.440971188
 
< 0.1%
-65.666416188
 
< 0.1%
-65.666866188
 
< 0.1%
-65.725097188
 
< 0.1%
-65.753897188
 
< 0.1%
Distinct3340
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.9 MiB
2025-11-30T20:08:44.441095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length55
Median length35
Mean length21.300599
Min length8

Characters and Unicode

Total characters13375072
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmerican Samoa, US
2nd rowGuam, US
3rd rowNorthern Mariana Islands, US
4th rowAdjuntas, Puerto Rico, US
5th rowAguada, Puerto Rico, US
ValueCountFrequency (%)
us623972
30.5%
texas48504
 
2.4%
virginia36096
 
1.8%
georgia30080
 
1.5%
north29704
 
1.5%
carolina28200
 
1.4%
new26508
 
1.3%
dakota23500
 
1.1%
kentucky22936
 
1.1%
south21808
 
1.1%
Other values (2049)1156576
56.5%
2025-11-30T20:08:44.938587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1419964
 
10.6%
,1254712
 
9.4%
a1117472
 
8.4%
n800880
 
6.0%
i785088
 
5.9%
o770236
 
5.8%
e737524
 
5.5%
S705752
 
5.3%
U651044
 
4.9%
s624160
 
4.7%
Other values (49)4508240
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)13375072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1419964
 
10.6%
,1254712
 
9.4%
a1117472
 
8.4%
n800880
 
6.0%
i785088
 
5.9%
o770236
 
5.8%
e737524
 
5.5%
S705752
 
5.3%
U651044
 
4.9%
s624160
 
4.7%
Other values (49)4508240
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13375072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1419964
 
10.6%
,1254712
 
9.4%
a1117472
 
8.4%
n800880
 
6.0%
i785088
 
5.9%
o770236
 
5.8%
e737524
 
5.5%
S705752
 
5.3%
U651044
 
4.9%
s624160
 
4.7%
Other values (49)4508240
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13375072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1419964
 
10.6%
,1254712
 
9.4%
a1117472
 
8.4%
n800880
 
6.0%
i785088
 
5.9%
o770236
 
5.8%
e737524
 
5.5%
S705752
 
5.3%
U651044
 
4.9%
s624160
 
4.7%
Other values (49)4508240
33.7%

Date
Date

Distinct188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Minimum2020-01-22 00:00:00
Maximum2020-07-27 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-30T20:08:45.100481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:45.430679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Confirmed
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct11091
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean357.28428
Minimum0
Maximum224051
Zeros253223
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-11-30T20:08:45.623200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q363
95-th percentile987
Maximum224051
Range224051
Interquartile range (IQR)63

Descriptive statistics

Standard deviation3487.2827
Coefficient of variation (CV)9.7605264
Kurtosis2053.3089
Mean357.28428
Median Absolute Deviation (MAD)4
Skewness39.356898
Sum2.2434595 × 108
Variance12161141
MonotonicityNot monotonic
2025-11-30T20:08:45.785070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0253223
40.3%
124942
 
4.0%
215883
 
2.5%
313001
 
2.1%
410569
 
1.7%
59394
 
1.5%
68403
 
1.3%
76962
 
1.1%
86341
 
1.0%
95542
 
0.9%
Other values (11081)273660
43.6%
ValueCountFrequency (%)
0253223
40.3%
124942
 
4.0%
215883
 
2.5%
313001
 
2.1%
410569
 
1.7%
59394
 
1.5%
68403
 
1.3%
76962
 
1.1%
86341
 
1.0%
95542
 
0.9%
ValueCountFrequency (%)
2240511
< 0.1%
2237611
< 0.1%
2235321
< 0.1%
2231921
< 0.1%
2228321
< 0.1%
2224441
< 0.1%
2220941
< 0.1%
2217031
< 0.1%
2214191
< 0.1%
2211211
< 0.1%

Deaths
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct2011
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.536328
Minimum0
Maximum23500
Zeros428930
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-11-30T20:08:45.966071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile34
Maximum23500
Range23500
Interquartile range (IQR)1

Descriptive statistics

Standard deviation300.99147
Coefficient of variation (CV)17.163882
Kurtosis4589.5806
Mean17.536328
Median Absolute Deviation (MAD)0
Skewness63.590888
Sum11011411
Variance90595.862
MonotonicityNot monotonic
2025-11-30T20:08:46.133516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0428930
68.3%
150238
 
8.0%
224941
 
4.0%
315125
 
2.4%
410637
 
1.7%
57450
 
1.2%
66652
 
1.1%
75225
 
0.8%
84606
 
0.7%
93912
 
0.6%
Other values (2001)70204
 
11.2%
ValueCountFrequency (%)
0428930
68.3%
150238
 
8.0%
224941
 
4.0%
315125
 
2.4%
410637
 
1.7%
57450
 
1.2%
66652
 
1.1%
75225
 
0.8%
84606
 
0.7%
93912
 
0.6%
ValueCountFrequency (%)
235001
< 0.1%
234851
< 0.1%
234761
< 0.1%
234651
< 0.1%
234631
< 0.1%
234281
< 0.1%
234241
< 0.1%
234111
< 0.1%
234001
< 0.1%
233881
< 0.1%

Interactions

2025-11-30T20:08:36.555673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:29.979095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:31.037470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:32.301646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:33.413644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:34.459988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:35.504014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:36.701371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:30.163160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:31.181557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:32.478603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:33.554792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:34.601124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:35.652760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:36.855943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:30.307187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:31.324706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:32.630693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:33.702420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:34.752583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:35.804449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:37.021715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:30.453668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:31.715748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:32.777088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:33.850768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:34.906859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:35.959083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:37.160326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:30.585644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:31.855967image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:32.933333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:34.003152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:35.046202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:36.107947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:37.313308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:30.742603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:32.013247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:33.091933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:34.152289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:35.201275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:36.261513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:37.450203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:30.898982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:32.154937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:33.260009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:34.295950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:35.347099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:36.413245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-11-30T20:08:46.252315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ConfirmedDeathsFIPSLatLong_UIDcode3iso2iso3
Confirmed1.0000.778-0.095-0.0360.106-0.0710.0420.0000.000
Deaths0.7781.000-0.117-0.0600.137-0.0620.1020.0000.000
FIPS-0.095-0.1171.000-0.0680.2020.868-0.2350.4260.426
Lat-0.036-0.060-0.0681.000-0.2910.0630.2480.6290.629
Long_0.1060.1370.202-0.2911.0000.068-0.2420.4930.493
UID-0.071-0.0620.8680.0630.0681.0000.2651.0001.000
code30.0420.102-0.2350.248-0.2420.2651.0001.0001.000
iso20.0000.0000.4260.6290.4931.0001.0001.0001.000
iso30.0000.0000.4260.6290.4931.0001.0001.0001.000

Missing values

2025-11-30T20:08:37.785741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-30T20:08:38.549802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-30T20:08:39.673933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

UIDiso2iso3code3FIPSAdmin2Province_StateCountry_RegionLatLong_Combined_KeyDateConfirmedDeaths
016ASASM1660.0NaNAmerican SamoaUS-14.271000-170.132000American Samoa, US1/22/2000
1316GUGUM31666.0NaNGuamUS13.444300144.793700Guam, US1/22/2000
2580MPMNP58069.0NaNNorthern Mariana IslandsUS15.097900145.673900Northern Mariana Islands, US1/22/2000
363072001PRPRI63072001.0AdjuntasPuerto RicoUS18.180117-66.754367Adjuntas, Puerto Rico, US1/22/2000
463072003PRPRI63072003.0AguadaPuerto RicoUS18.360255-67.175131Aguada, Puerto Rico, US1/22/2000
563072005PRPRI63072005.0AguadillaPuerto RicoUS18.459681-67.120815Aguadilla, Puerto Rico, US1/22/2000
663072007PRPRI63072007.0Aguas BuenasPuerto RicoUS18.251619-66.126806Aguas Buenas, Puerto Rico, US1/22/2000
763072009PRPRI63072009.0AibonitoPuerto RicoUS18.131361-66.264131Aibonito, Puerto Rico, US1/22/2000
863072011PRPRI63072011.0AnascoPuerto RicoUS18.287985-67.120611Anasco, Puerto Rico, US1/22/2000
963072013PRPRI63072013.0AreciboPuerto RicoUS18.406631-66.675077Arecibo, Puerto Rico, US1/22/2000
UIDiso2iso3code3FIPSAdmin2Province_StateCountry_RegionLatLong_Combined_KeyDateConfirmedDeaths
62791084070002USUSA840NaNDukes and NantucketMassachusettsUS41.406747-70.687635Dukes and Nantucket,Massachusetts,US7/27/209524
62791184070003USUSA840NaNKansas CityMissouriUS39.099700-94.578600Kansas City,Missouri,US7/27/2049493
62791284070004USUSA840NaNMichigan Department of Corrections (MDOC)MichiganUS0.0000000.000000Michigan Department of Corrections (MDOC), Michigan, US7/27/20412468
62791384070005USUSA840NaNFederal Correctional Institution (FCI)MichiganUS0.0000000.000000Federal Correctional Institution (FCI), Michigan, US7/27/201925
62791484070015USUSA840NaNBear RiverUtahUS41.521068-113.083282Bear River, Utah, US7/27/2020995
62791584070016USUSA840NaNCentral UtahUtahUS39.372319-111.575868Central Utah, Utah, US7/27/203471
62791684070017USUSA840NaNSoutheast UtahUtahUS38.996171-110.701396Southeast Utah, Utah, US7/27/20700
62791784070018USUSA840NaNSouthwest UtahUtahUS37.854472-111.441876Southwest Utah, Utah, US7/27/20278123
62791884070019USUSA840NaNTriCountyUtahUS40.124915-109.517442TriCounty, Utah, US7/27/201420
62791984070020USUSA840NaNWeber-MorganUtahUS41.271160-111.914512Weber-Morgan, Utah, US7/27/20237524